Economic Forecastingis a process by which businesses predict the future economicsituation. It involves the use of statistical models that utilizes variables called indicators. Some of the economic indicators used include retail sales and unemployment rates, GDP decline / growth, inflation, and interest rates…
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Therefore, financial managers should be capabletoidentify and respond promptly to the impact of competitive forces and changes affecting their businesses. Most organizations fail to forecast on the fiscal impact of these changes fast enough; thus, affecting their businesses. Periodical forecasts take two to five weeks to finalize, and budgets are normally not finalized until the actual year of budgeting. The time taken to produce each repetition of the budget or forecast is too long taking days and sometimes weeks, and in the current environment, the impact of any change to the financials need to be understood within a short period(Elliott, Granger, and Timmermann, 2006).It is startling that the need for faster delivery of advanced forecasts and budgets has not received more consideration more so in light of the time and drive toward a faster close Most budgeting and forecasting processes and systemsrequireacceptablechanges to accommodate thereorganizations, mergers, acquisition, and divestitures that have become the characteristic of modern businesses. This creates historical comparisons and trend analyses that old questionable value. In addition, most systems are not flexible enough to accommodate the demand for multiple views of forecast and budget information. Nonetheless, some of the elements that need to be considered in the economic forecasting are discussed herein below. 1) Time Series Plot of sales This pattern exists when a series is influenced by seasonal factors includingdays of the week, months and the quarters of the year. There are usuallycumulative trend and seasonal peak between the index andindex 15. Several methods of analysis are possible, for example: 1. Exponentially smoothing and Forecasting Convenient that allows frequent information 2. Season /Trend decomposition is Simple and easy to communicate 3. Time series estimate and modelingis the most advanced and is not easily communicated 4. Regression with seasonal models, flexible and Intermediate Simple season or trend decomposition does not pick up possible monthly auto-correlation as the other methods do. Regression with seasonal and trend modelshasadded opportunity of groupingtogether months of comparable sales level to give a model with few factors and with potentially better forecasts. 2)Descriptive Statistics: Sales Variable N N* Mean SEMean StDev MinimumQ1 MedianQ3 Maximum Sales 16 0 5370 257 1030 3896 44605288 6010 7707 Autocorrelation Function: Sales Lag ACF T LBQ 1 0.058206 0.23 0.07 2 0.181312 0.72 0.74 3 -0.001481 -0.01 0.74 4 0.552103 2.13 8.06 Quantity connection function is obtained when the quantity in high-level item is alteredwith the quantity of sub-item. When thisis done, the change in the proportion is realized automatically, and the Sales orderSales ACF linkage and delivery group can be updated manually that is defined in the above graph that in turn defines the following conditions: 1. Sales ACF linkage existed without delivery group then the extent correlation function is active. 2. Delivery group existed without sales ACF linkage then the extent correlation function is inactive. 3. Sales ACF linkage existed with delivery group then the extent correlation function is active. If the delivery Sales ACF linkage is updated manually then delivery group cannot be updated then, 1. Sales ACF link
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“Economic Forecasting Research Paper Example | Topics and Well Written Essays - 1000 Words”, n.d. https://studentshare.org/macro-microeconomics/1486367-economic-forecasting.
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However, it is
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